This is the report of the analysis made for the paper (TITLE) AND AUTHORS. INSERT ABSTRACT
Importing data and filtering out those genes with cpm lesser than 1. We use the filtered.data method in NOISeq package.
countMatrix <- ReadDataFrameFromTsv(file.name.path="../../data/refSEQ_countMatrix.txt")
## ../../data/refSEQ_countMatrix.txt read from disk!
# head(countMatrix)
designMatrix <- ReadDataFrameFromTsv(file.name.path="../../design/all_samples_short_names_noRS2HC7.tsv")
## ../../design/all_samples_short_names_noRS2HC7.tsv read from disk!
# head(designMatrix)
filteredCountsProp <- filterLowCounts(counts.dataframe=countMatrix,
is.normalized=FALSE,
design.dataframe=designMatrix,
cond.col.name="gcondition",
method.type="Proportion")
## features dimensions before normalization: 27179
## Filtering out low count features...
## 14454 features are to be kept for differential expression analysis with filtering method 3
PCA Plot of filtered not-normalized data.
PlotPCAPlotlyFunction(counts.data.frame=log1p(filteredCountsProp),
design.matrix=designMatrix,
shapeColname="condition", colorColname="genotype", xPCA="PC1", yPCA="PC2",
plotly.flag=TRUE, show.plot.flag=TRUE, prefix.plot="Prop-Un-Norm")
## [1] TRUE
Loading Negative Control Genes to normalize data
library(readxl)
sd.ctrls <- read_excel(path="../../data/controls/Additional File 4 full list of BMC genomics SD&RS2.xlsx", sheet=1)
sd.ctrls <- sd.ctrls[order(sd.ctrls$adj.P.Val),]
sd.neg.ctrls <- sd.ctrls[sd.ctrls$adj.P.Val > 0.9, ]
sd.neg.ctrls <- sd.neg.ctrls$`MGI Symbol`
sd.neg.ctrls <- sd.neg.ctrls[-which(is.na(sd.neg.ctrls))]
int.neg.ctrls <- sd.neg.ctrls
int.neg.ctrls <- unique(int.neg.ctrls)
neg.map <- convertGenesViaMouseDb(gene.list=int.neg.ctrls, fromType="SYMBOL",
"ENTREZID")
# sum(is.na(neg.map$ENTREZID))
neg.ctrls.entrez <- as.character(neg.map$ENTREZID)
ind.ctrls <- which(rownames(filteredCountsProp) %in% neg.ctrls.entrez)
counts.neg.ctrls <- filteredCountsProp[ind.ctrls,]
Loading Positive Control Genes to detect them during the differential expression step.
## sleep deprivation
sd.lit.pos.ctrls <- read_excel("../../data/controls/SD_RS_PosControls_final.xlsx",
sheet=1)
colnames(sd.lit.pos.ctrls) <- sd.lit.pos.ctrls[1,]
sd.lit.pos.ctrls <- sd.lit.pos.ctrls[-1,]
sd.est.pos.ctrls <- read_excel("../../data/controls/SD_RS_PosControls_final.xlsx",
sheet=3)
sd.pos.ctrls <- cbind(sd.est.pos.ctrls$`MGI Symbol`, "est")
sd.pos.ctrls <- rbind(sd.pos.ctrls, cbind(sd.lit.pos.ctrls$Gene, "lit"))
sd.pos.ctrls <- sd.pos.ctrls[-which(duplicated(sd.pos.ctrls[,1])),]
sd.pos.ctrls <- sd.pos.ctrls[-which(is.na(sd.pos.ctrls[,1])),]
Normalizing data with TMM, as implemented in edgeR package, and plotting a PCA and an RLE plot of them.
normPropCountsUqua <- NormalizeData(data.to.normalize=filteredCountsProp,
norm.type="tmm",
design.matrix=designMatrix)
PlotPCAPlotlyFunction(counts.data.frame=log1p(normPropCountsUqua),
design.matrix=designMatrix, shapeColname="condition",
colorColname="genotype", xPCA="PC1", yPCA="PC2",
plotly.flag=TRUE, show.plot.flag=TRUE,
prefix.plot="TMM-Norm")
## [1] TRUE
pal <- RColorBrewer::brewer.pal(9, "Set1")
plotRLE(as.matrix(normPropCountsUqua), outline=FALSE, col=pal[designMatrix$gcondition])
Applying a RUVs method of RUVSeq package on normalized data, in order to adjust the counts for the unwanted variation. And of corse we plot a PCA and an RLE plot on these data.
library(RUVSeq)
neg.ctrl.list <- rownames(counts.neg.ctrls)
groups <- makeGroups(designMatrix$gcondition)
ruvedSExprData <- RUVs(as.matrix(round(normPropCountsUqua)), cIdx=neg.ctrl.list,
scIdx=groups, k=5)
normExprData <- ruvedSExprData$normalizedCounts
ggp <- PlotPCAPlotlyFunction(counts.data.frame=log1p(normExprData),
design.matrix=designMatrix, shapeColname="condition",
colorColname="genotype", xPCA="PC1", yPCA="PC2",
plotly.flag=FALSE, show.plot.flag=FALSE, save.plot=FALSE,
prefix.plot=NULL)
## [1] FALSE
ggplotly(ggp)
dir.create("plots")
save_plot(filename="plots/PCA.pdf", plot=ggp)
pal <- RColorBrewer::brewer.pal(9, "Set1")
plotRLE(normExprData, outline=FALSE, col=pal[designMatrix$gcondition])
Making differential expression analysis with edgeR package on four different contrasts.
Here is a brief legend: * WTHC5: Wild Type Home Cage Control 5 days * WTSD5: Wild Type Sleep Deprivation 5 days. * KOHC5: Knock Out Home Cage Control 5 days. * KOSD5: Knock Out Sleep Deprivation 5 days.
padj.thr <- 0.05
venn.padgj.thr <- 0.1
desMat <- cbind(designMatrix, ruvedSExprData$W)
colnames(desMat) <- c(colnames(designMatrix), colnames(ruvedSExprData$W))
cc <- c("WTSD5 - WTHC5", "S3HC5 - WTHC5",
"S3SD5 - WTSD5", "S3SD5 - S3HC5")
rescList1 <- applyEdgeR(counts=filteredCountsProp, design.matrix=desMat,
factors.column="gcondition",
weight.columns=c("W_1", "W_2", "W_3", "W_4", "W_5"),
contrasts=cc, useIntercept=FALSE, p.threshold=1,
is.normalized=FALSE, verbose=TRUE)
names <- names(rescList1)
rescList1 <- lapply(seq_along(rescList1), function(i)
{
attachMeans(normalized.counts=normExprData, design.matrix=desMat,
factor.column="gcondition", contrast.name=names(rescList1)[i],
de.results=rescList1[[i]])
})
names(rescList1) <- names
An histogram of pvalues.
PlotHistPvalPlot(de.results=rescList1[[1]], design.matrix=desMat,
show.plot.flag=TRUE, plotly.flag=TRUE,
prefix.plot=names(rescList1)[1])
A volcano plot of differential expressed genes.
res.o.map1 <- convertGenesViaMouseDb(gene.list=rownames(rescList1[[1]]),
fromType="ENTREZID")
res.o <- attachGeneColumnToDf(mainDf=rescList1[[1]],
genesMap=res.o.map1,
rowNamesIdentifier="ENTREZID",
mapFromIdentifier="ENTREZID",
mapToIdentifier="SYMBOL")
WriteDataFrameAsTsv(data.frame.to.save=res.o,
file.name.path=paste0(names(rescList1)[1], "_edgeR"))
vp <- luciaVolcanoPlot(res.o, sd.pos.ctrls, prefix=names(rescList1)[1],
threshold=padj.thr)
ggplotly(vp)
de <- sum(res.o$FDR < padj.thr)
nde <- sum(res.o$FDR >= padj.thr)
detable <- cbind(de,nde)
rownames(detable) <- names(rescList1)[1]
ddetable <- detable
tot.ctrls <- dim(sd.pos.ctrls)[1]
idx.pc <- which(tolower(res.o$gene) %in% tolower(sd.pos.ctrls[,1]))
tot.pc.de <- sum(res.o$FDR[idx.pc] < padj.thr)
tot.pc.nde <- length(idx.pc) - tot.pc.de
pos.df <- cbind(tot.ctrls, tot.pc.de, tot.pc.nde)
colnames(pos.df) <- c("total_p.ctrl", "p.ctrl_de_mapped",
"p.ctrl_notde_mapped")
rownames(pos.df) <- names(rescList1)[1]
wt <- res.o[which(res.o$FDR < padj.thr),]
wt.sign.genes.entrez <- rownames(res.o)[which(res.o$FDR < venn.padgj.thr)]
An histogram of pvalues.
PlotHistPvalPlot(de.results=rescList1[[2]], design.matrix=desMat,
show.plot.flag=TRUE, plotly.flag=TRUE,
prefix.plot=names(rescList1)[2])
A volcano plot of differential expressed genes.
rs2.o.map <- convertGenesViaMouseDb(gene.list=rownames(rescList1[[2]]),
fromType="ENTREZID")
res.rs2.o <- attachGeneColumnToDf(mainDf=rescList1[[2]],
genesMap=rs2.o.map,
rowNamesIdentifier="ENTREZID",
mapFromIdentifier="ENTREZID",
mapToIdentifier="SYMBOL")
WriteDataFrameAsTsv(data.frame.to.save=res.rs2.o,
file.name.path=paste0(names(rescList1)[2], "_edgeR"))
vp <- luciaVolcanoPlot(res.rs2.o, positive.controls.df=NULL,
prefix=names(rescList1)[2],
threshold=padj.thr)
ggplotly(vp)
de <- sum(res.rs2.o$FDR < padj.thr)
nde <- sum(res.rs2.o$FDR >= padj.thr)
detable <- cbind(de,nde)
rownames(detable) <- names(rescList1)[2]
ddetable <- rbind(ddetable, detable)
kowthc5 <- res.rs2.o[which(res.rs2.o$FDR < padj.thr),]
kowthc5.sign.genes.entrez <- rownames(res.rs2.o)[which(res.rs2.o$FDR < venn.padgj.thr)]
An histogram of pvalues.
PlotHistPvalPlot(de.results=rescList1[[3]], design.matrix=desMat,
show.plot.flag=TRUE, plotly.flag=TRUE,
prefix.plot=names(rescList1)[3])
A volcano plot of differential expressed genes.
res.o.map <- convertGenesViaMouseDb(gene.list=rownames(rescList1[[3]]),
fromType="ENTREZID")
res.o <- attachGeneColumnToDf(mainDf=rescList1[[3]],
genesMap=res.o.map,
rowNamesIdentifier="ENTREZID",
mapFromIdentifier="ENTREZID",
mapToIdentifier="SYMBOL")
WriteDataFrameAsTsv(data.frame.to.save=res.o,
file.name.path=paste0(names(rescList1)[3], "_edgeR"))
vp <- luciaVolcanoPlot(res.o, positive.controls.df=sd.pos.ctrls,
prefix=names(rescList1)[3], threshold=padj.thr)
ggplotly(vp)
de <- sum(res.o$FDR < padj.thr)
nde <- sum(res.o$FDR >= padj.thr)
detable <- cbind(de,nde)
rownames(detable) <- names(rescList1)[3]
ddetable <- rbind(ddetable, detable)
tot.ctrls <- dim(sd.pos.ctrls)[1]
idx.pc <- which(tolower(res.o$gene) %in% tolower(sd.pos.ctrls[,1]))
tot.pc.de <- sum(res.o$FDR[idx.pc] < padj.thr)
tot.pc.nde <- length(idx.pc) - tot.pc.de
pos.dff <- cbind(tot.ctrls, tot.pc.de, tot.pc.nde)
rownames(pos.dff) <- names(rescList1)[3]
pos.df <- rbind(pos.df, pos.dff)
kowtsd5 <- res.o[which(res.o$FDR < padj.thr),]
kowtsd5.sign.genes.entrez <- rownames(res.o)[which(res.o$FDR < venn.padgj.thr)]
An histogram of pvalues.
PlotHistPvalPlot(de.results=rescList1[[4]], design.matrix=desMat,
show.plot.flag=TRUE, plotly.flag=TRUE,
prefix.plot=names(rescList1)[4])
A volcano plot of differential expressed genes.
res.o.map <- convertGenesViaMouseDb(gene.list=rownames(rescList1[[4]]),
fromType="ENTREZID")
res.o <- attachGeneColumnToDf(mainDf=rescList1[[4]],
genesMap=res.o.map,
rowNamesIdentifier="ENTREZID",
mapFromIdentifier="ENTREZID",
mapToIdentifier="SYMBOL")
WriteDataFrameAsTsv(data.frame.to.save=res.o,
file.name.path=paste0(names(rescList1)[4], "_edgeR"))
vp <- luciaVolcanoPlot(res.o, positive.controls.df=sd.pos.ctrls,
prefix=names(rescList1)[4], threshold=padj.thr)
ggplotly(vp)
de <- sum(res.o$FDR < padj.thr)
nde <- sum(res.o$FDR >= padj.thr)
detable <- cbind(de,nde)
rownames(detable) <- names(rescList1)[4]
ddetable <- rbind(ddetable, detable)
tot.ctrls <- dim(sd.pos.ctrls)[1]
idx.pc <- which(tolower(res.o$gene) %in% tolower(sd.pos.ctrls[,1]))
tot.pc.de <- sum(res.o$FDR[idx.pc] < padj.thr)
tot.pc.nde <- length(idx.pc) - tot.pc.de
pos.dff <- cbind(tot.ctrls, tot.pc.de, tot.pc.nde)
rownames(pos.dff) <- names(rescList1)[2]
pos.df <- rbind(pos.df, pos.dff)
ko <- res.o[which(res.o$FDR < padj.thr),]
ko.sign.genes.entrez <- rownames(res.o)[which(res.o$FDR < venn.padgj.thr)]
We present a summarization of the results. The first table is a summarization on how many genes are Differentially Expressed. The second table explains on the first column how many positive controls we have, on the second column how many positive controls have been identified over the differentially expressed genes, and, finally, on the third column how many positive controls have beed identified on the NOT differentially expressed genes.
ddetable
## de nde
## WTSD5 - WTHC5 5650 8804
## S3HC5 - WTHC5 39 14415
## S3SD5 - WTSD5 82 14372
## S3SD5 - S3HC5 5772 8682
pos.df
## total_p.ctrl p.ctrl_de_mapped p.ctrl_notde_mapped
## WTSD5 - WTHC5 579 454 102
## S3SD5 - WTSD5 579 4 552
## S3HC5 - WTHC5 579 452 104
We take the results of two contrasts. Knock Out Sleed Deprivation VS Wild Type Sleep Deprivation and Knock Out Home Cage control VS Wild Type Home Cage Controls . And plot the results in a Venn Diagram
source("../../R/venn2.R")
gene.map <- convertGenesViaMouseDb(gene.list=rownames(normExprData),
fromType="ENTREZID", toType="SYMBOL")
venn <- Venn2de(x=kowthc5.sign.genes.entrez, y=kowtsd5.sign.genes.entrez,
label1="S3HC5_WTHC5", label2="S3SD5_WTSD5",
title="S3HC5_WTHC5 venn S3SD5_WTSD5", plot.dir="./",
conversion.map=gene.map)
An heatmap of the union of the genes identified.
source("../../R/heatmapFunctions.R")
de.genes.entr <- union(rownames(venn$int), rownames(venn$XnoY))
de.genes.entr <- union(de.genes.entr, rownames(venn$YnoX))
gene.map <- convertGenesViaMouseDb(gene.list=de.genes.entr,
fromType="ENTREZID")
de.genes.symb <- attachGeneColumnToDf(as.data.frame(de.genes.entr,
row.names=de.genes.entr),
genesMap=gene.map,
rowNamesIdentifier="ENTREZID",
mapFromIdentifier="ENTREZID",
mapToIdentifier="SYMBOL")
# de.genes.symb[which(is.na(de.genes.symb$gene)),]
de.genes.symb$gene[which(de.genes.symb$de.genes.entr=="100039826")] <- "Gm2444" ## not annotated in ncbi
de.genes.symb$gene[which(de.genes.symb$de.genes.entr=="210541")] <- "Entrez:210541" ## not annotated in ncbi
de.genes.counts <- normExprData[match(de.genes.symb$de.genes.entr, rownames(normExprData)),]
rownames(de.genes.counts) <- de.genes.symb$gene
de.gene.means <- computeGeneMeansOverGroups(counts=de.genes.counts,
design=designMatrix, groupColumn="gcondition")
library(gplots)
library(clusterExperiment)
color.palette = clusterExperiment::seqPal3#c("black", "yellow")
pal <- colorRampPalette(color.palette)(n = 1000)
# table(filter)
library(pheatmap)
filter2 <- rowMeans(de.gene.means)>0
ph1 <- pheatmap(log(de.gene.means[filter2,]+1), cluster_cols=FALSE, scale="row", color=pal, border_color=NA, fontsize_row=5)
save_pheatmap_pdf(filename="plots/heatmap_union_genes.pdf", plot=ph1)
## png
## 2
Heatmap of a group of genes which present inverse trends between Wild Type and Knock Out conditions.
filter <- apply(de.gene.means, 1, function(x) log(x[4]/x[3]) * log(x[2]/x[1]) < 0)
filter[is.na(filter)] <- FALSE
ph2 <- pheatmap(log(de.gene.means[filter,]+1), cluster_cols=FALSE, scale="row", color=pal, border_color=NA, fontsize_row=8)
save_pheatmap_pdf(filename="plots/heatmap_genes_two_trends.pdf", plot=ph2)
## png
## 2
The trends of the genes identified in the second heatmap.
source("../../R/plotGeneProfile.R")
g <- geneProfileLucia(normalized.counts=normExprData,
design.matrix=designMatrix,
gene.name="Gm7984",
res.o=de.genes.symb, show.plot=TRUE, plotly.flag=FALSE)
ggplotly(g)
save_plot(filename=paste0("plots/", "Gm7984", "_gene_profile.pdf"), plot=g)
g <- geneProfileLucia(normalized.counts=normExprData,
design.matrix=designMatrix,
gene.name="Mgat4b",
res.o=de.genes.symb, show.plot=TRUE, plotly.flag=FALSE)
ggplotly(g)
save_plot(filename=paste0("plots/", "Mgat4b", "_gene_profile.pdf"), plot=g)
g <- geneProfileLucia(normalized.counts=normExprData,
design.matrix=designMatrix,
gene.name="Ano6",
res.o=de.genes.symb, show.plot=TRUE, plotly.flag=FALSE)
ggplotly(g)
save_plot(filename=paste0("plots/", "Ano6", "_gene_profile.pdf"), plot=g)
g <- geneProfileLucia(normalized.counts=normExprData,
design.matrix=designMatrix,
gene.name="Zbtb21",
res.o=de.genes.symb, show.plot=TRUE, plotly.flag=FALSE)
ggplotly(g)
save_plot(filename=paste0("plots/", "Zbtb21", "_gene_profile.pdf"), plot=g)
g <- geneProfileLucia(normalized.counts=normExprData,
design.matrix=designMatrix,
gene.name="Sfxn1",
res.o=de.genes.symb, show.plot=TRUE, plotly.flag=FALSE)
ggplotly(g)
save_plot(filename=paste0("plots/", "Sfxn1", "_gene_profile.pdf"), plot=g)
g <- geneProfileLucia(normalized.counts=normExprData,
design.matrix=designMatrix,
gene.name="Plxnb2",
res.o=de.genes.symb, show.plot=TRUE, plotly.flag=FALSE)
ggplotly(g)
save_plot(filename=paste0("plots/", "Plxnb2", "_gene_profile.pdf"), plot=g)
g <- geneProfileLucia(normalized.counts=normExprData,
design.matrix=designMatrix,
gene.name="Gng4",
res.o=de.genes.symb, show.plot=TRUE, plotly.flag=FALSE)
ggplotly(g)
save_plot(filename=paste0("plots/", "Gng4", "_gene_profile.pdf"), plot=g)
g <- geneProfileLucia(normalized.counts=normExprData,
design.matrix=designMatrix,
gene.name="Slc6a13",
res.o=de.genes.symb, show.plot=TRUE, plotly.flag=FALSE)
ggplotly(g)
save_plot(filename=paste0("plots/", "Slc6a13", "_gene_profile.pdf"), plot=g)
g <- geneProfileLucia(normalized.counts=normExprData,
design.matrix=designMatrix,
gene.name="Pdyn",
res.o=de.genes.symb, show.plot=TRUE, plotly.flag=FALSE)
ggplotly(g)
save_plot(filename=paste0("plots/", "Pdyn", "_gene_profile.pdf"), plot=g)
g <- geneProfileLucia(normalized.counts=normExprData,
design.matrix=designMatrix,
gene.name="Reln",
res.o=de.genes.symb, show.plot=TRUE, plotly.flag=FALSE)
ggplotly(g)
save_plot(filename=paste0("plots/", "Reln", "_gene_profile.pdf"), plot=g)
g <- geneProfileLucia(normalized.counts=normExprData,
design.matrix=designMatrix,
gene.name="Rpl29",
res.o=de.genes.symb, show.plot=TRUE, plotly.flag=FALSE)
ggplotly(g)
save_plot(filename=paste0("plots/", "Rpl29", "_gene_profile.pdf"), plot=g)
g <- geneProfileLucia(normalized.counts=normExprData,
design.matrix=designMatrix,
gene.name="Ryr1",
res.o=de.genes.symb, show.plot=TRUE, plotly.flag=FALSE)
ggplotly(g)
save_plot(filename=paste0("plots/", "Ryr1", "_gene_profile.pdf"), plot=g)
g <- geneProfileLucia(normalized.counts=normExprData,
design.matrix=designMatrix,
gene.name="Rnd1",
res.o=de.genes.symb, show.plot=TRUE, plotly.flag=FALSE)
ggplotly(g)
save_plot(filename=paste0("plots/", "Rnd1", "_gene_profile.pdf"), plot=g)
g <- geneProfileLucia(normalized.counts=normExprData,
design.matrix=designMatrix,
gene.name="Plekha6",
res.o=de.genes.symb, show.plot=TRUE, plotly.flag=FALSE)
ggplotly(g)
save_plot(filename=paste0("plots/", "Plekha6", "_gene_profile.pdf"), plot=g)
g <- geneProfileLucia(normalized.counts=normExprData,
design.matrix=designMatrix,
gene.name="Mapk1",
res.o=de.genes.symb, show.plot=TRUE, plotly.flag=FALSE)
ggplotly(g)
save_plot(filename=paste0("plots/", "Mapk1", "_gene_profile.pdf"), plot=g)
g <- geneProfileLucia(normalized.counts=normExprData,
design.matrix=designMatrix,
gene.name="Slc7a11",
res.o=de.genes.symb, show.plot=TRUE, plotly.flag=FALSE)
ggplotly(g)
save_plot(filename=paste0("plots/", "Slc7a11", "_gene_profile.pdf"), plot=g)
g <- geneProfileLucia(normalized.counts=normExprData,
design.matrix=designMatrix,
gene.name="Gprin1",
res.o=de.genes.symb, show.plot=TRUE, plotly.flag=FALSE)
ggplotly(g)
save_plot(filename=paste0("plots/", "Gprin1", "_gene_profile.pdf"), plot=g)
g <- geneProfileLucia(normalized.counts=normExprData,
design.matrix=designMatrix,
gene.name="Cttnbp2",
res.o=de.genes.symb, show.plot=TRUE, plotly.flag=FALSE)
ggplotly(g)
save_plot(filename=paste0("plots/", "Cttnbp2", "_gene_profile.pdf"), plot=g)
g <- geneProfileLucia(normalized.counts=normExprData,
design.matrix=designMatrix,
gene.name="Jmy",
res.o=de.genes.symb, show.plot=TRUE, plotly.flag=FALSE)
ggplotly(g)
save_plot(filename=paste0("plots/", "Jmy", "_gene_profile.pdf"), plot=g)
g <- geneProfileLucia(normalized.counts=normExprData,
design.matrix=designMatrix,
gene.name="Nek6",
res.o=de.genes.symb, show.plot=TRUE, plotly.flag=FALSE)
ggplotly(g)
save_plot(filename=paste0("plots/", "Nek6", "_gene_profile.pdf"), plot=g)
g <- geneProfileLucia(normalized.counts=normExprData,
design.matrix=designMatrix,
gene.name="Dusp10",
res.o=de.genes.symb, show.plot=TRUE, plotly.flag=FALSE)
ggplotly(g)
save_plot(filename=paste0("plots/", "Dusp10", "_gene_profile.pdf"), plot=g)
g <- geneProfileLucia(normalized.counts=normExprData,
design.matrix=designMatrix,
gene.name="Zmynd19",
res.o=de.genes.symb, show.plot=TRUE, plotly.flag=FALSE)
ggplotly(g)
save_plot(filename=paste0("plots/", "Zmynd19", "_gene_profile.pdf"), plot=g)
g <- geneProfileLucia(normalized.counts=normExprData,
design.matrix=designMatrix,
gene.name="Kcnv1",
res.o=de.genes.symb, show.plot=TRUE, plotly.flag=FALSE)
ggplotly(g)
save_plot(filename=paste0("plots/", "Kcnv1", "_gene_profile.pdf"), plot=g)
g <- geneProfileLucia(normalized.counts=normExprData,
design.matrix=designMatrix,
gene.name="Ddit4l",
res.o=de.genes.symb, show.plot=TRUE, plotly.flag=FALSE)
ggplotly(g)
save_plot(filename=paste0("plots/", "Ddit4l", "_gene_profile.pdf"), plot=g)
g <- geneProfileLucia(normalized.counts=normExprData,
design.matrix=designMatrix,
gene.name="Fggy",
res.o=de.genes.symb, show.plot=TRUE, plotly.flag=FALSE)
ggplotly(g)
save_plot(filename=paste0("plots/", "Fggy", "_gene_profile.pdf"), plot=g)
g <- geneProfileLucia(normalized.counts=normExprData,
design.matrix=designMatrix,
gene.name="Sytl1",
res.o=de.genes.symb, show.plot=TRUE, plotly.flag=FALSE)
ggplotly(g)
save_plot(filename=paste0("plots/", "Sytl1", "_gene_profile.pdf"), plot=g)
g <- geneProfileLucia(normalized.counts=normExprData,
design.matrix=designMatrix,
gene.name="Bmp1",
res.o=de.genes.symb, show.plot=TRUE, plotly.flag=FALSE)
ggplotly(g)
save_plot(filename=paste0("plots/","Bmp1", "_gene_profile.pdf"), plot=g)
g <- geneProfileLucia(normalized.counts=normExprData,
design.matrix=designMatrix,
gene.name="Otof",
res.o=de.genes.symb, show.plot=TRUE, plotly.flag=FALSE)
ggplotly(g)
save_plot(filename=paste0("plots/","Otof", "_gene_profile.pdf"), plot=g)
g <- geneProfileLucia(normalized.counts=normExprData,
design.matrix=designMatrix,
gene.name="Syt7",
res.o=de.genes.symb, show.plot=TRUE, plotly.flag=FALSE)
ggplotly(g)
save_plot(filename=paste0("plots/","Syt7", "_gene_profile.pdf"), plot=g)
g <- geneProfileLucia(normalized.counts=normExprData,
design.matrix=designMatrix,
gene.name="Chia1",
res.o=de.genes.symb, show.plot=TRUE, plotly.flag=FALSE)
ggplotly(g)
save_plot(filename=paste0("plots/","Chia1", "_gene_profile.pdf"), plot=g)
g <- geneProfileLucia(normalized.counts=normExprData,
design.matrix=designMatrix,
gene.name="Tuba1c",
res.o=de.genes.symb, show.plot=TRUE, plotly.flag=FALSE)
ggplotly(g)
save_plot(filename=paste0("plots/","Tuba1c", "_gene_profile.pdf"), plot=g)
g <- geneProfileLucia(normalized.counts=normExprData,
design.matrix=designMatrix,
gene.name="Olfr287",
res.o=de.genes.symb, show.plot=TRUE, plotly.flag=FALSE)
ggplotly(g)
save_plot(filename=paste0("plots/","Olfr287", "_gene_profile.pdf"), plot=g)
g <- geneProfileLucia(normalized.counts=normExprData,
design.matrix=designMatrix,
gene.name="Gm7984",
res.o=de.genes.symb, show.plot=TRUE, plotly.flag=FALSE)
ggplotly(g)
save_plot(filename=paste0("plots/","Gm7984", "_gene_profile.pdf"), plot=g)
g <- geneProfileLucia(normalized.counts=normExprData,
design.matrix=designMatrix,
gene.name="Mgat4b",
res.o=de.genes.symb, show.plot=TRUE, plotly.flag=FALSE)
ggplotly(g)
save_plot(filename=paste0("plots/","Mgat4b", "_gene_profile.pdf"), plot=g)
g <- geneProfileLucia(normalized.counts=normExprData,
design.matrix=designMatrix,
gene.name="Gprin1",
res.o=de.genes.symb, show.plot=TRUE, plotly.flag=FALSE)
ggplotly(g)
save_plot(filename=paste0("plots/","Gprin1", "_gene_profile.pdf"), plot=g)
g <- geneProfileLucia(normalized.counts=normExprData,
design.matrix=designMatrix,
gene.name="Atp6v0c",
res.o=de.genes.symb, show.plot=TRUE, plotly.flag=FALSE)
ggplotly(g)
save_plot(filename=paste0("plots/","Atp6v0c", "_gene_profile.pdf"), plot=g)
g <- geneProfileLucia(normalized.counts=normExprData,
design.matrix=designMatrix,
gene.name="Jun",
res.o=de.genes.symb, show.plot=TRUE, plotly.flag=FALSE)
ggplotly(g)
save_plot(filename=paste0("plots/","Jun", "_gene_profile.pdf"), plot=g)
g <- geneProfileLucia(normalized.counts=normExprData,
design.matrix=designMatrix,
gene.name="Ddit4l",
res.o=de.genes.symb, show.plot=TRUE, plotly.flag=FALSE)
ggplotly(g)
save_plot(filename=paste0("plots/","Ddit4l", "_gene_profile.pdf"), plot=g)
g <- geneProfileLucia(normalized.counts=normExprData,
design.matrix=designMatrix,
gene.name="Pdyn",
res.o=de.genes.symb, show.plot=TRUE, plotly.flag=FALSE)
ggplotly(g)
save_plot(filename=paste0("plots/","Pdyn", "_gene_profile.pdf"), plot=g)
g <- geneProfileLucia(normalized.counts=normExprData,
design.matrix=designMatrix,
gene.name="Sema3a",
res.o=de.genes.symb, show.plot=TRUE, plotly.flag=FALSE)
ggplotly(g)
save_plot(filename=paste0("plots/","Sema3a", "_gene_profile.pdf"), plot=g)
g <- geneProfileLucia(normalized.counts=normExprData,
design.matrix=designMatrix,
gene.name="Gng4",
res.o=de.genes.symb, show.plot=TRUE, plotly.flag=FALSE)
ggplotly(g)
save_plot(filename=paste0("plots/","Gng4", "_gene_profile.pdf"), plot=g)
g <- geneProfileLucia(normalized.counts=normExprData,
design.matrix=designMatrix,
gene.name="Reln",
res.o=de.genes.symb, show.plot=TRUE, plotly.flag=FALSE)
ggplotly(g)
save_plot(filename=paste0("plots/","Reln", "_gene_profile.pdf"), plot=g)
g <- geneProfileLucia(normalized.counts=normExprData,
design.matrix=designMatrix,
gene.name="Kcnv1",
res.o=de.genes.symb, show.plot=TRUE, plotly.flag=FALSE)
ggplotly(g)
save_plot(filename=paste0("plots/","Kcnv1", "_gene_profile.pdf"), plot=g)
Profiles of some known genes.
g <- geneProfileLucia(normalized.counts=normExprData,
design.matrix=designMatrix,
gene.name="Nr1d1",
res.o=de.genes.symb, show.plot=TRUE, plotly.flag=FALSE)
ggplotly(g)
save_plot(filename=paste0("plots/", "Nr1d1", "_gene_profile.pdf"), plot=g)
g <- geneProfileLucia(normalized.counts=normExprData,
design.matrix=designMatrix,
gene.name="Per3",
res.o=de.genes.symb, show.plot=TRUE, plotly.flag=FALSE)
ggplotly(g)
save_plot(filename=paste0("plots/", "Per3", "_gene_profile.pdf"), plot=g)
g <- geneProfileLucia(normalized.counts=normExprData,
design.matrix=designMatrix,
gene.name="Fabp7",
res.o=de.genes.symb, show.plot=TRUE, plotly.flag=FALSE)
ggplotly(g)
save_plot(filename=paste0("plots/", "Fabp7", "_gene_profile.pdf"), plot=g)
g <- geneGroupProfile(normalized.counts=normExprData, design.matrix=designMatrix,
gene.names=c("Nr1d1", "Fabp7", "Per3"),
res.o=de.genes.symb, show.plot=TRUE, plotly.flag=FALSE, log.flag=FALSE)
ggplotly(g)
save_plot(filename=paste0("plots/", "Nr1d1_Fabp7_Per3", "_gene_profile.pdf"), plot=g)
g <- geneGroupProfile(normalized.counts=normExprData, design.matrix=designMatrix,
gene.names=c("Jun", "Elk1", "Fosl2", "Mapk1", "Mapk3", "Mapk11"),
res.o=de.genes.symb, show.plot=TRUE, plotly.flag=FALSE, log.flag=FALSE)
ggplotly(g)
save_plot(filename=paste0("plots/", "Jun_Elk1_Fosl2_Mapk", "_gene_profile.pdf"), plot=g)
g <- geneGroupProfile(normalized.counts=normExprData, design.matrix=designMatrix,
gene.names=c("Nr1d1", "Fabp7", "Per3"),
res.o=de.genes.symb, show.plot=TRUE, plotly.flag=FALSE, log.flag=TRUE)
ggplotly(g)
save_plot(filename=paste0("plots/", "Nr1d1_Fabp7_Per3", "_log_gene_profile.pdf"), plot=g)
g <- geneGroupProfile(normalized.counts=normExprData, design.matrix=designMatrix,
gene.names=c("Jun", "Elk1", "Fosl2", "Mapk1", "Mapk3", "Mapk11"),
res.o=de.genes.symb, show.plot=TRUE, plotly.flag=FALSE, log.flag=TRUE)
ggplotly(g)
save_plot(filename=paste0("plots/", "Jun_Elk1_Fosl2_Mapk", "_log_gene_profile.pdf"), plot=g)